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Deep Learning for Climate Science

Climate change is one of the most important problems facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to understand the evolution of the climate system subject to various CO2 emission scenarios. Unfortunately, large scale climate simulations produce 100TB-sized spatio-temporal, multi-variate datasets, making it difficult to conduct sophisticated analytics. In this talk, I will present our results in applying Deep Learning for semi-supervised learning of extreme weather patterns. I will conclude with a summary of current applications of Deep Learning for a broad set of scientific use cases, and open research challenges for the future.

Prabhat leads the Data and Analytics Services team at NERSC. His current research interests applied statistics, machine learning, and high performance computing. He has worked on topics in scientific data management, parallel I/O, scientific visualization, computer graphics and computer vision in the past. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.

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